Due Date: 05.01.2020
Instructor: Mustafa Baydogan
Group Members:
Sinan Demirhan
Abdullah Yildiz
Ogulcan Ece
Merve Keskin
Merve Gülsüm Kiratli
Oguzhan Murat TOSUN
library(jpeg)
require(stats)
library("imager")
## Loading required package: magrittr
##
## Attaching package: 'imager'
## The following object is masked from 'package:magrittr':
##
## add
## The following objects are masked from 'package:stats':
##
## convolve, spectrum
## The following object is masked from 'package:graphics':
##
## frame
## The following object is masked from 'package:base':
##
## save.image
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library("EBImage")
##
## Attaching package: 'EBImage'
## The following objects are masked from 'package:imager':
##
## channel, dilate, display, erode, resize, watershed
library(wvtool)
getwd()
## [1] "C:/Users/Sinan/Desktop"
setwd("C:/Users/Sinan/Desktop")
im<- load.image('Fabric1.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=15, theta=100, bw=10, phi=0, asp=0.3, disp=TRUE)
hist(test$filtered_img)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K)/3)
K
## [1] 3.338733
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric2.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=8, theta=60, bw=10, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 3.80219
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric3.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=8, theta=60, bw=10, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 4.154883
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric4.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=15, theta=80, bw=10, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 5.280681
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric5.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=4, theta=20, bw=3, phi=1, asp=3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K)/3)
K
## [1] 2.126587
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric6.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=8, theta=60, bw=10, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 3.095354
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric7.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=15, theta=30, bw=10, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 3.479602
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric8.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=8, theta=10, bw=5, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 3.103828
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric9.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=15, theta=60, bw=10, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 3.348351
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)
im<- load.image('Fabric10.jpg')
img_g<-grayscale(im)
plot(img_g)
data<-as.data.frame(as.cimg(img_g))[,3]
data<-array(data,dim=c(512,512))
new_image<-data
test <- gabor.filter(x=data,lamda=8, theta=20, bw=10, phi=0, asp=0.3, disp=TRUE)
arr<-test$filtered_img
K<-(max(as.vector(arr))-min(as.vector(arr)))/sqrt(var(as.vector(arr)))
K<-((K-1)/3)
K
## [1] 3.211053
upper<-mean(arr)+K*sqrt(var(as.vector(arr)))
lower<-mean(arr)-K*sqrt(var(as.vector(arr)))
plot(as.vector(arr),type ="p",ylab = "Pixel values",xlab ="Pixels",main = "Control Chart for Pixels")+
abline(h=upper,col="blue") +
abline(h=lower,col="blue") +
abline(h=mean(arr),col="red")
## integer(0)
for(i in c(1:512)){
for (j in c(1:512)) {
if((arr[i,j]<lower) ||(arr[i,j]>upper) ){
new_image[i,j]<-0
}
}
}
pixel_for_dim<- as.data.frame(img_g)
pixel_for_dim[,3]<-as.vector(new_image)
pixels_with_defects<-pixel_for_dim[,3] %>% as.cimg(dim=dim(img_g))
par(mfrow = c(1,2))
plot(1:2, type='n',main = "ORIGINAL IMAGE")
rasterImage(img_g, 1, 1, 2, 2)
plot(1:2, type='n',main = "IMAGE DEFECTS FOUNDED")
rasterImage(pixels_with_defects, 1, 1, 2, 2)